Google Cloud
Teradata to BigQuery Migration Services
Move from Teradata to BigQuery with an assessment-led plan, automated SQL translation, verified data and a cutover your business can sign off with confidence.
Leave Teradata Without Leaving Anything Behind
Teradata has powered enterprise reporting for decades, but fixed-capacity hardware, renewal cycles and scarce specialist skills make it expensive to keep. BigQuery offers serverless scale, separation of storage and compute, built-in machine learning and native integration with Looker and Google Cloud AI. The hard part is not loading tables; it is moving years of BTEQ scripts, stored procedures, macros, views and downstream dependencies without breaking the reports people rely on every morning.
Hanoi Technology runs Teradata to BigQuery migrations as engineering programs. We use BigQuery Migration Service for assessment and SQL translation, the BigQuery Data Transfer Service and its migration agent for schema and data movement, and automated validation to prove that every table and key report matches. Where a straight translation would carry forward old inefficiencies, we redesign for BigQuery’s strengths.
Governance is part of the program from the first week. A shared object inventory tracks every table, view, macro, procedure, script and report, with its owner, migration wave and validation status. Steering meetings review risks and decisions, and weekly written updates show progress against the plan. When the last wave is signed off, you have both a working BigQuery platform and a complete record of how it was built and verified.
The Challenge
Why Teradata Migrations Are Hard
These are the obstacles that stretch timelines if they are not planned for.
Large Volumes of Procedural SQL
Workloads often include thousands of BTEQ scripts, macros and stored procedures with Teradata-specific syntax such as QUALIFY, SET tables and implicit casting. Each needs translation and testing, not just conversion.
Hidden Dependencies
ETL tools, schedulers, BI reports and ad hoc user queries all reach into the warehouse. Missing one consumer means a broken report after cutover.
Different Performance Model
Teradata relies on primary indexes and AMP distribution, while BigQuery relies on partitioning, clustering and slot capacity. Copying physical designs as-is leads to slow queries and higher costs.
Proving the Data Is Right
Business owners need evidence that row counts, aggregates and key metrics match before they agree to switch off the old system. Without automated validation, reconciliation becomes a manual bottleneck. Sign-off then drags on and both platforms run longer than planned.
Business Value
What You Gain on BigQuery
No More Capacity Planning
BigQuery scales compute automatically or through reservations, so month-end and seasonal peaks no longer require new hardware.
Lower Total Cost of Ownership
You stop paying for idle capacity and hardware refreshes, and choose between on-demand and edition-based pricing per workload.
Modern Analytics and AI
BigQuery ML, Gemini integration and native connections to Looker make advanced analytics available without moving data.
Open Data Access
BigLake and Apache Iceberg tables let other engines read the same data, reducing lock-in and duplicate copies.
Simpler Operations
No vacuuming, statistics collection, node management or upgrades: Google operates the service and you focus on data.
Built-In Governance
Column-level security, row-level access policies, policy tags and Knowledge Catalog lineage support compliance from day one.
Translate What You Must, Redesign What You Should
Automated translation handles the bulk of SQL reliably, but a migration is also the best chance in years to remove dead tables, collapse redundant pipelines and fix performance hacks. We separate the two deliberately so you get speed and improvement.
Typical improvements include replacing volatile tables with common table expressions, collapsing chains of staging jobs into incremental Dataform models, and redesigning wide reporting tables around partition and cluster keys that match real query filters.
Our Services
Hanoi Technology’s End-to-End Teradata to BigQuery Migration Services
End-to-end services for every phase of a Teradata exit.
Migration Assessment
We run the BigQuery migration assessment on Teradata logs and metadata to profile objects, query patterns, usage and translation complexity, then build a business case and wave plan.
- Object and workload inventory
- Complexity and usage analysis
- Wave plan and target architecture
SQL and BTEQ Translation
We use batch SQL translation and the translation API to convert DDL, DML, BTEQ and stored procedures at scale, then resolve remaining manual items and optimize queries.
- Translated SQL repository
- Translation issue log
- Optimized critical queries
Schema and Data Transfer
We configure the BigQuery Data Transfer Service with Teradata Parallel Transporter or JDBC extraction, custom schema files for partitioning and clustering, and incremental loads during parallel running.
- Transfer configurations
- Custom schema mapping
- Incremental load jobs
Pipeline and ETL Migration
We repoint or rebuild Informatica, DataStage or script-based ETL as Dataform, Dataflow or Cloud Composer pipelines with monitoring and alerting.
- Re-platformed pipelines
- Orchestration DAGs
- Operational monitoring
Validation and Reconciliation
We use the open-source Data Validation Tool and custom checks to compare row counts, column aggregates and row hashes between Teradata and BigQuery.
- Automated validation suite
- Reconciliation reports
- Business sign-off pack
Cutover and Decommissioning
We plan consumer redirection, BI repointing, freeze windows and rollback, then support the business through the first close cycles on BigQuery.
- Cutover runbook
- Hypercare support
- Decommissioning checklist
Our Approach
Our Five-Phase Migration Method
Each wave moves through the same controlled phases.
- 01
Assess and Plan
We collect query logs and metadata, run the migration assessment and group workloads into waves by dependency and business priority.
Deliverables: Assessment report · Wave plan
- 02
Design the Target
We define datasets, partitioning and clustering, security model, reservation strategy and pipeline tooling on BigQuery.
Deliverables: Target architecture · Security and cost design
- 03
Translate and Move
We translate SQL in bulk, transfer historical data, and set up incremental loads so both systems stay current during testing.
Deliverables: Translated code · Loaded and synchronized data
- 04
Validate in Parallel
We run both platforms side by side, compare outputs automatically and fix discrepancies until business owners sign off.
Deliverables: Validation results · Signed-off reports
- 05
Cut Over and Optimize
We redirect consumers, monitor performance and cost closely, and tune the platform before decommissioning Teradata.
Deliverables: Cutover completion · Post-migration tuning
Use Cases
Typical Migration Scenarios
Situations where clients commonly start a Teradata to BigQuery program.
Contract Renewal Deadline
A company facing a hardware refresh and license renewal plans a phased exit so the most expensive workloads move before the renewal date.
Finance Close Reporting
A finance team’s month-end BTEQ jobs are translated, tuned and validated against two closing cycles before Teradata is retired.
Retail Sales Analytics
Point-of-sale history spanning years moves to partitioned BigQuery tables, cutting query times for store-level comparisons.
Telecom Usage Data
High-volume call detail records shift to BigQuery with streaming ingestion through Pub/Sub, replacing nightly batch loads.
Consolidating Data Marts
Several departmental marts built on Teradata are rationalized into one governed BigQuery platform with shared dimensions.
Enabling Machine Learning
After migration, data science teams build churn and demand models directly with BigQuery ML instead of exporting extracts.
Technology
Migration Tooling
Google-provided and open-source tools we use on Teradata programs.
- BigQuery
- BigQuery Migration Service
- Batch SQL Translator
- Interactive SQL Translator
- BigQuery Data Transfer Service
- Teradata Parallel Transporter
- Data Validation Tool
- Cloud Storage
- Dataform
- Dataflow
- Cloud Composer
- Pub/Sub
- Looker
- Terraform
Platforms
Deliver Teradata to BigQuery Migration on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
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Start Your Teradata Exit Plan
We can assess your Teradata workload and outline a realistic migration path. Book a free consultation and we will reply within 24 hours.
Teradata to BigQuery Migration FAQ
How much of our Teradata SQL can be translated automatically?
Google states that Teradata SQL is fully supported by BigQuery’s batch and interactive SQL translation services, and in practice the large majority of DDL and DML converts cleanly. The remaining effort usually lies in complex stored procedures, BTEQ control flow, performance tuning and behavior differences such as case sensitivity or rounding. The assessment quantifies this before we commit to estimates.
How long does a Teradata to BigQuery migration take?
It depends on data volume, number of objects, ETL complexity and how long parallel running must last. A focused data mart can move in a few months; a full enterprise warehouse is typically planned over multiple waves. We give a wave-based timeline after the assessment, with the earliest waves chosen to show value quickly and reduce risk.
Does the data transfer service move stored procedures and views?
No. The BigQuery Data Transfer Service moves table schemas and data, but stored procedures, views, saved queries and UDFs are not transferred. Those go through SQL translation and testing as part of the code migration workstream. We track every object in an inventory so nothing is silently left behind.
How do you keep both systems in sync during testing?
We perform an initial full load, then schedule incremental transfers using commit timestamp or primary key annotations in a custom schema file, or use pipeline-based change capture where needed. Because the transfer service does not sync hard deletes, we add reconciliation steps for tables where deletions matter.
What happens to our BI tools and ETL jobs?
Most BI tools connect natively to BigQuery, so reports are repointed and tested rather than rebuilt. ETL is either repointed if the tool supports BigQuery or re-platformed to Dataform, Dataflow or Cloud Composer. The dependency inventory from the assessment ensures every consumer is planned into a wave.
How is data protected during the migration?
Extracted files are not encrypted by the migration agent, so we restrict access to local staging, use dedicated service accounts, keep Cloud Storage buckets private with retention and lifecycle rules, and delete staging data after loading. BigQuery datasets are secured with IAM, policy tags and audit logging before any data arrives.